Integrated clustering of multiple immune marker trajectories reveals different immunotypes in severely injured patients.

BACKGROUND: The immune response of critically ill patients, such as those with sepsis, severe trauma, or major surgery, is heterogeneous and dynamic, but its characterization and impact on outcomes are poorly understood. Until now, the primary challenge in advancing our understanding of the disease has been to concurrently address both multiparametric and temporal aspects. METHODS: We used a clustering method to identify distinct groups of patients, based on various immune marker trajectories during the first week after admission to ICU. In 339 severely injured patients, we initially longitudinally clustered common biomarkers (both soluble and cellular parameters), whose variations are well-established during the immunosuppressive phase of sepsis. We then applied this multi-trajectory clustering using markers composed of whole blood immune-related mRNA. RESULTS: We found that both sets of markers revealed two immunotypes, one of which was associated with worse outcomes, such as increased risk of hospital-acquired infection and mortality, and prolonged hospital stays. This immunotype showed signs of both hyperinflammation and immunosuppression, which persisted over time. CONCLUSION: Our study suggest that the immune system of critically ill patients can be characterized by two distinct longitudinal immunotypes, one of which included patients with a persistently dysregulated and impaired immune response. This work confirms the relevance of such methodology to stratify patients and pave the way for further studies using markers indicative of potential immunomodulatory drug targets.

Immune profiling of critically ill patients with acute kidney injury during the first week after various types of injuries: the REALAKI study.

BACKGROUND: Acute kidney injury (AKI) is common in hospitalized patients and results in significant morbidity and mortality. The objective of the study was to explore the systemic immune response of intensive care unit patients presenting with AKI, especially the association between immune profiles and persistent AKI during the first week after admission following various types of injuries (sepsis, trauma, surgery, and burns). METHODS: REALAKI is an ancillary analysis of the REAnimation Low Immune Status Marker (REALISM) cohort study, in which 359 critically ill patients were enrolled in three different intensive care units. Patients with end-stage renal disease were excluded from the REALAKI study. Clinical samples and data were collected three times after admission: at day 1 or 2 (D1-2), day 3 or 4 (D3-4) and day 5, 6 or 7 (D5-7). Immune profiles were compared between patients presenting with or without AKI. Patients with AKI at both D1-2 and D5-7 were defined as persistent AKI. A multivariable logistic regression model was performed to determine the independent association between AKI and patients' immunological parameters. RESULTS: Three hundred and fifty-nine patients were included in this analysis. Among them, 137 (38%) were trauma patients, 103 (29%) post-surgery patients, 95 (26%) sepsis patients, and 24 (7%) were burn patients. One hundred and thirty-nine (39%) patients presented with AKI at D1-2 and 61 (20%) at D5-7. Overall, 94% presented with persistent AKI at D5-7. Patients with AKI presented with increased pro and anti-inflammatory cytokines and altered innate and adaptive immune responses. The modifications observed in the immune profiles tended to be more pronounced with increasing KDIGO stages. In the logistic regression model, a statistically significant association was observed at D1-2 between AKI and CD10lowCD16low immature neutrophils (OR 3.03 [1.7-5.5]-p < 0.001). At D5-7, increased interleukin-10 (IL-10) levels and reduced ex vivo TNF-α production after LPS stimulation were significantly associated with the presence of AKI (OR 1.38 [1.12-1.71]-p = 0.001 and 0.51 [0.27-0.91]-p = 0.03, respectively). Patients who recovered from AKI between D1-2 and D5-7 compared to patients with persistent AKI at D5-7, tended to correct these alterations. CONCLUSION: Following various types of severe injuries, early AKI is associated with the initial inflammatory response. Presence of AKI at the end of the first week after injury is associated with injury-induced immunosuppression.

Enhancing sepsis biomarker development: key considerations from public and private perspectives.

Implementation of biomarkers in sepsis and septic shock in emergency situations, remains highly challenging. This viewpoint arose from a public-private 3-day workshop aiming to facilitate the transition of sepsis biomarkers into clinical practice. The authors consist of international academic researchers and clinician-scientists and industry experts who gathered (i) to identify current obstacles impeding biomarker research in sepsis, (ii) to outline the important milestones of the critical path of biomarker development and (iii) to discuss novel avenues in biomarker discovery and implementation. To define more appropriately the potential place of biomarkers in sepsis, a better understanding of sepsis pathophysiology is mandatory, in particular the sepsis patient's trajectory from the early inflammatory onset to the late persisting immunosuppression phase. This time-varying host response urges to develop time-resolved test to characterize persistence of immunological dysfunctions. Furthermore, age-related difference has to be considered between adult and paediatric septic patients. In this context, numerous barriers to biomarker adoption in practice, such as lack of consensus about diagnostic performances, the absence of strict recommendations for sepsis biomarker development, cost and resources implications, methodological validation challenges or limited awareness and education have been identified. Biomarker-guided interventions for sepsis to identify patients that would benefit more from therapy, such as sTREM-1-guided Nangibotide treatment or Adrenomedullin-guided Enibarcimab treatment, appear promising but require further evaluation. Artificial intelligence also has great potential in the sepsis biomarker discovery field through capability to analyse high volume complex data and identify complex multiparametric patient endotypes or trajectories. To conclude, biomarker development in sepsis requires (i) a comprehensive and multidisciplinary approach employing the most advanced analytical tools, (ii) the creation of a platform that collaboratively merges scientific and commercial needs and (iii) the support of an expedited regulatory approval process.

Use of Immune Profiling Panel to assess the immune response of septic patients for prediction of worsening as a composite endpoint.

Sepsis induces intense, dynamic and heterogeneous host response modulations. Despite improvement of patient management, the risk of mortality and healthcare-associated infections remains high. Treatments to counterbalance immune response are under evaluation, but effective biomarkers are still lacking to perform patient stratification. The design of the present study was defined to alleviate the limitations of existing literature: we selected patients who survived the initial hyperinflammatory response and are still hospitalized at day 5-7 after ICU admission. Using the Immune Profiling Panel (IPP), a fully automated RT-qPCR multiplex prototype, we optimized a machine learning model combining the IPP gene expression levels for the identification of patients at high risk of worsening, a composite endpoint defined as death or secondary infection, within one week after sampling. This was done on 332 sepsis patients selected from two retrospective studies. The IPP model identified a high-risk group comprising 30% of patients, with a significant increased proportion of worsening events at day 28 compared to the low-risk group (49% vs. 28%, respectively). These preliminary results underline the potential clinical application of IPP for sepsis patient stratification in a personalized medicine perspective, that will be confirmed in a larger prospective multicenter study.